import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import torch.utils.data as data
import torch.nn.functional as F
from torch.autograd import Variable


EPOCH = 12
BATCH_SIZE = 5
LR = 0.1

x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)
y = x.pow(2) + 0.1 * torch.normal(torch.zeros(*x.size()))

# plt.scatter(x.numpy(), y.numpy())
# plt.show()

torch_dataset = data.TensorDataset(data_tensor=x, target_tensor=y)
loader = data.DataLoader(dataset=torch_dataset,
                         batch_size=BATCH_SIZE,
                         shuffle=True,
                         num_workers=2)


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.hidden = nn.Linear(1, 20)
        self.predict = nn.Linear(20, 1)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x



net_SGD = Net()
net_Momentum = Net()
net_RMSprop = Net()
net_Adam = Net()

nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]

opt_SGD = optim.SGD(net_SGD.parameters(), lr=LR)
opt_Momentum = optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)
opt_RMSprop = optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)
opt_Adam = optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))

optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]

loss_func = nn.MSELoss()

loss_history = [[], [], [], []]

for epoch in range(EPOCH):
    print(epoch)
    for step, (batch_x, batch_y) in enumerate(loader):
        b_x = Variable(batch_x)
        b_y = Variable(batch_y)

        for net, opt, l_h, in zip(nets, optimizers, loss_history):
            output = net(b_x)
            loss = loss_func(output, b_y)
            opt.zero_grad()
            loss.backward()
            opt.step()
            
            l_h.append(loss.data[0])


labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']

for i, l_h in enumerate(loss_history):
    plt.plot(l_h, label=labels[i])

plt.legend(loc='best')
plt.xlabel('Steps')
plt.ylabel('Loss')
plt.ylim(0, 0.2)
plt.show()





